A hierarchical algorithm for image multi-labeling

Jiwei Hu; Kin Man Lam; Guoping Qiu

doi:10.1109/ICIP.2010.5653434

A hierarchical algorithm for image multi-labeling

Jiwei Hu, Kin Man Lam, Guoping Qiu

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

11 Citations (Scopus)

Abstract

This paper presents an efficient two-stage method for multiclass image labeling. We first propose a simple label-filtering algorithm (LFA), which can remove most of the irrelevant labels for a query image while the potential labels are maintained. With a small population of potential labels left, we then apply the Naive-Bayes Nearest-Neighbor (NBNN) classifier as the second stage of our algorithm to identify the labels for the query image. This approach has been evaluated on the Corel database, and compared to existing algorithms. Experiment results show that our proposed algorithm can achieve a promising result, as it outperforms existing algorithms.

Original language	English
Title of host publication	2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Pages	2349-2352
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2010.5653434
Publication status	Published - 1 Dec 2010
Event	2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong Duration: 26 Sept 2010 → 29 Sept 2010

Conference

Conference	2010 17th IEEE International Conference on Image Processing, ICIP 2010
Country/Territory	Hong Kong
City	Hong Kong
Period	26/09/10 → 29/09/10

Keywords

Label filtering
Multi-label classification
Nearest neighbors

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

More information

10.1109/ICIP.2010.5653434

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title = "A hierarchical algorithm for image multi-labeling",

abstract = "This paper presents an efficient two-stage method for multiclass image labeling. We first propose a simple label-filtering algorithm (LFA), which can remove most of the irrelevant labels for a query image while the potential labels are maintained. With a small population of potential labels left, we then apply the Naive-Bayes Nearest-Neighbor (NBNN) classifier as the second stage of our algorithm to identify the labels for the query image. This approach has been evaluated on the Corel database, and compared to existing algorithms. Experiment results show that our proposed algorithm can achieve a promising result, as it outperforms existing algorithms.",

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AB - This paper presents an efficient two-stage method for multiclass image labeling. We first propose a simple label-filtering algorithm (LFA), which can remove most of the irrelevant labels for a query image while the potential labels are maintained. With a small population of potential labels left, we then apply the Naive-Bayes Nearest-Neighbor (NBNN) classifier as the second stage of our algorithm to identify the labels for the query image. This approach has been evaluated on the Corel database, and compared to existing algorithms. Experiment results show that our proposed algorithm can achieve a promising result, as it outperforms existing algorithms.

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KW - Nearest neighbors

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M3 - Conference article published in proceeding or book

SN - 9781424479948

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BT - 2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings

T2 - 2010 17th IEEE International Conference on Image Processing, ICIP 2010

Y2 - 26 September 2010 through 29 September 2010

ER -

A hierarchical algorithm for image multi-labeling

Abstract

Conference

Keywords

ASJC Scopus subject areas

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